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Chain Reduction for Binary and Zero-Suppressed Decision Diagrams

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Abstract

Chain reduction enables reduced ordered binary decision diagrams (BDDs) and zero-suppressed binary decision diagrams (ZDDs) to each take advantage of the other’s ability to symbolically represent Boolean functions in compact form. For any Boolean function, its chain-reduced ZDD (CZDD) representation will be no larger than its ZDD representation, and at most twice the size of its BDD representation. The chain-reduced BDD (CBDD) of a function will be no larger than its BDD representation, and at most three times the size of its CZDD representation. Extensions to the standard algorithms for operating on BDDs and ZDDs enable them to operate on the chain-reduced versions. Experimental evaluations on representative benchmarks for encoding word lists, solving combinatorial problems, and operating on digital circuits indicate that chain reduction can provide significant benefits in terms of both memory and execution time. The experimental results are further validated by a quantitative model of how decision diagrams scale when encoding sets of sequences. This model explains why the combination of a one-hot encoding of the symbols in the sequences, plus a CBDD, CZDD, or ZDD representation of the set, yields the most compact form.

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Acknowledgements

This work has benefited from conversations with Shin-Ichi Minato and Ofer Strichman. This work was supported, in part, by NSF STARSS Grant 1525527.

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  1. Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Randal E. Bryant

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Bryant, R.E. Chain Reduction for Binary and Zero-Suppressed Decision Diagrams. J Autom Reasoning 64, 1361–1391 (2020). https://doi.org/10.1007/s10817-020-09569-6

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